A Study of the Artificial Neural Network for Rainfall-Runoff Process
Resource
農業工程學報 43(1),9-25
Journal
農業工程學報
Pages
9-25
Date Issued
1997-03
Date
1997-03
Author(s)
Suen, Jian-Ping
Abstract
With the improvement of science, the characters of biological brains were investigated and implemented to modern technology. The artificial neural network (ANN) has been developed through the conceptual of biological brain characters and shown to be capable of self-organization and self-learning to describe non-linear systems. Due to the flexible structure and simplex organization of the ANN, it could approximately simulate any complex continuous input-output mapping. Consequently, the method is used to investigate the hydrological events which have the characters of highly uncertainty, non-uniform, and randomness. In this study, the back-propagation neural network with three learning network layers, i.e. input, hidden, and output, is utilized to forecast the hourly rainfall and to simulate the rainfall-runoff process. Meanwhile the effect of input number of the rainfall-runoff process is also investigate. The results indicate that the neural network is capable to describe the complex hydrological events and has great forecasting efficiency.
Subjects
ANN
Non-linear system
Rainfall-runoff process
Type
journal article
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類神經網路及應用於降雨-逕流過程之研究.pdf
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